The risk of algorithmic entropy – When AI models age faster than evolving fraud
Using artificial intelligence (AI) to tackle fraud is now a strategic priority for CFOs. But unless carefully managed, these models risk ageing faster than the fraud they are meant to detect, writes Majid Mumtaz, an independent ex-Big Four consultant with years of leadership experience in internal audit.
Fraud detection in the GCC runs on machine learning. As digital maturity accelerates, machine learning models are no longer a luxury; they are the engine of high-velocity commerce.
However, while fraud models age, the economies in which they operate do not wait. In the GCC, where growth is reshaping transaction patterns, ownership structures, and regulatory frameworks simultaneously, the gap between a model’s training environment and its current operating reality grows faster than most organisations expect.
This ‘model drift’ – the gradual divergence between a model’s training assumptions and its current operating reality – is producing material fraud losses, compliance failures, and significant ‘governance debt’ for the region’s leading enterprises.
To understand how this algorithmic entropy erodes value, one must look at the specific commercial archetypes where growth is currently outpacing governance.
Digital Transformation: When Speed Outpaces Model Validity
The first and most pervasive risk is found in the region’s digital-first platforms, where operational scale has outstripped model oversight. Ramadan and Eid surges alone can push food delivery and payment volumes to multiples of their daily average. New aggregator channels get added mid-season. Pricing structures change.
Each shift alters the statistical environment that a fraud model was trained to read, through data drift, where input distributions change, and concept drift, where the relationship between those inputs and actual fraud evolves. A model calibrated six months ago is scoring today’s transactions against conditions it was never designed for.
These inaccuracies manifest as a binary operational risk: inflationary false positives that effectively turn a fraud prevention tool into a ‘revenue suppression engine’, blocking loyal customers during their highest-spending month. For Internal Audit, this represents a failure of traditional ITGCs (information technology general controls).
Legacy controls are designed for deterministic logic; they are ill-equipped to detect the entropy of an algorithmic baseline that shifts silently without a single line of code being changed.
In UAE remittance corridors, model failure has a third consequence, settlement delays that travel downstream through payment processors and partner networks. As the UAE’s Personal Data Protection Law (PDPL) regime develops enforcement precedent around algorithmic accountability, the distance between deploying a model and governing its ongoing performance is where liability accumulates.
However, the risk is not confined to organic growth; it is also being imported through the region’s aggressive deal-making landscape.
M&A Due Diligence: The Inherited Model Problem
The GCC’s M&A market is running hot. Family offices, sovereign-backed entities, and private equity are actively acquiring tech-enabled businesses across food & beverage, logistics, and real estate.
Yet, every machine learning model is a ‘Box of Assumptions’ shaped by its birthplace. In the rush to close, post-merger integration (PMI) often overlooks the technical debt inherent in these inherited algorithms. For GCC acquirers, failing to audit these systems leads to immediate post-deal value leakage.
Acquired models carry assumptions baked in at training time, assumptions shaped by a data environment that may look nothing like the acquirer’s. A model built on UAE transaction patterns performs differently on a Saudi family group’s data, where fraud vectors, regulatory metadata, and transaction structures differ.
As a result, post-integration drift follows. Control gaps created during integration consistently elevate fraud exposure above pre-deal levels, a cost that rarely appears in deal models.
The gap in standard due diligence practice is real. Financial audit of an acquisition target is rigorous by convention. Scrutiny of the machine learning systems embedded in that target is not. Sovereign wealth funds pursuing outbound acquisitions across the region are inheriting model risk they have not quantified. This ‘inherited drift’ often acts as the first domino in a broader collapse of enterprise-wide risk frameworks.
ERM Integration: When Portfolio Diversification Outpaces Risk Models
For the region’s diversified conglomerates, portfolio growth creates a structural drift problem. When a group acquires a cloud kitchen chain alongside a logistics business and a fintech operation, it adds fraud typologies that its existing risk management-embedded machine learning models were never trained to recognise.
As Saudi giga-projects transition from blueprint to bulk transaction, the sheer velocity of new transaction data will accelerate the drift lifecycle far beyond traditional benchmarks.
From an Internal Audit perspective, these drifted models create a governance blind spot. When executive committees make capital allocation decisions based on stale risk signatures, they are effectively flying blind in a high-growth environment. For conglomerates with listed entities, Saudi Arabia’s CMA Corporate Governance Regulations make the gap between reported and actual risk positions more than a governance failure, it carries direct enforcement exposure.
In conglomerate structures, the propagation effect matters. A drift-related fraud event in one vertical depresses investor confidence across the portfolio. AI models embedded in enterprise risk management (ERM) frameworks need drift governance as a standing control, built in from the start. This is particularly critical for entities looking to move beyond private ownership toward the public markets.
IPO Readiness: Model Drift and the Cost of Compliance Failure
GCC tech unicorns on the path to the Dubai Financial Market, Tadawul, or Nasdaq lean heavily on machine learning models to demonstrate SOX 404 and ITGC compliance. In my experience auditing these entities, there is a pervasive and dangerous assumption that a model passing controls testing today will keep passing. Drift is the stress test they have not planned for.
The timing risk is acute. As an Internal Audit Director, I have seen ‘audit-ready’ models crumble during external testing. When an auditor finds that drift has dropped model accuracy from 95% to 70%, it is flagged as a ‘Material Weakness’ in controls. That triggers disclosure requirements, valuation renegotiations, and a delayed listing.
In markets where IPO windows are short and investor confidence is non-negotiable, a single failed audit cycle can push a listing back twelve to eighteen months. The advisory opportunity, building drift-resistant compliance infrastructure that holds through an audit cycle, is real. But beyond the financial stakes, there is a growing regulatory dimension that organisations can no longer ignore.
Governance and Ethics: When Drift Becomes a Regulatory Event
The regulatory landscape is shifting as fast as the models. The UAE’s Personal Data Protection Law (PDPL) is live. Saudi Arabia’s SDAIA has published AI ethics principles and generative AI guidelines. In this context, model drift is no longer a maintenance failure; it is a potential regulatory event.
Fraud models that drift in the GCC’s diverse labour markets can develop systematic scoring biases. Under UAE PDPL, the ‘Right to Explanation’ means that if a drifted model erroneously blocks a legitimate transaction, the liability attaches to the model’s behaviour, not its original design intent. Fines can reach AED 5 million for serious violations.
A high-profile bias incident from a drifted fraud model does not stay contained. It surfaces in policy discussions, shapes how foreign technology partners assess regional market risk, and damages the technology credibility that national programmes like Vision 2030 are built on.
Consulting practices that can audit model fairness and help clients navigate this regulatory frontier are in early-mover territory. The regulatory frameworks are moving faster than most practices realise.
Conclusion
Fraud models age. Economies move faster. In the GCC, rapid growth is reshaping transactions, ownership and regulation, widening the gap between how models were trained and the reality they now face.
Closing this gap requires governance structures, monitoring frameworks, and adaptive validation protocols that treat drift as a standing operational risk, not a problem to diagnose once the losses have already landed. The transition from reactive remediation to proactive governance is the next frontier for the region’s fight against fraud.
About the Author: Majid Mumtaz has over 20 years of internal audit leadership experience in the GCC across consulting, multinationals, and scale-ups.
